Goals

I will try to discern what a typical simulation looks like. I attempt to do this by looking at many simulations under the same parameters. There are 110 simulations for a combination. I don’t think 100 repeated simulations will cover all the possible outcomes, but it will be a good beginning.

The first set of simulations I will look at parameters of sigma = 0.2, mu = 1.5625e-10. I will look at the means and standard deviation for each of the simulations over the 1000 generations. I am going to specifically look at the similarities and differences between each of theses simulations. I predict that they will look similar at the beginning and vary as the amount of generations increases.

Newts and snakes should evenly have the larger phenotype (be about 50-50).

thinking of making a data frame for each thing to look at columns -> generation, rows -> trials

Phenotype and SD Comparisons in Newts and Snakes

To start I am going to compare newts and snakes starting and ending phenotypes. Put more specifically, I will look at each simulation and record which species had a higher phenotype at the beginning and end of each of the simulations. The hope is that both newts and snakes have an equal (random) opportunity to have a higher phenotype. The ending could be skewed if coevoultion took place (but it is unknown, and I predict that the phenotypes will be close at 1000gen because it isn’t a long time). I will look at the difference in the mean phenotype and the difference in standard deviations.

It looks like newts had a higher mean and sd in the beginning of the simulation more than half of the trials that I ran. Probably not statistically significant (thought is to run more simulations to see or look at the other combos that I ran and see if it still happens there). The ending phenotypes are strange. I do not really know if there is something happening or if the advantage from the beginning of the run is leading to a newt advantage. I want to look at the other parameters to see if this is a reoccurring trend. The species with a higher SD remained close to 50%, in fact it seems to have gotten closer to each other ass the simulations ran on.

Varation in the Phenotype of Newts and Snakes

Next, I want to look at the distribution of mean phenotype values for every 100th generation. This will look into how the mean phenotype changes over time. I can look at the distributions of the mean and the standard deviation of the phenotype. I predict that the means will increase. The slop of the increase should be do to the interaction between species (maybe a larger increase could be obtained by stronger selection?). In my simulations there is interspecific an intraspecific interactions. I wonder which one (or both?) is having more of an effect on the populations phenotype.

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

It seems like the mean phenotype of the newts and snakes are not exploding when using this set of parameters. They are however going up very slowly. It does look like there is more variation in the average phenotypes and sd as the number of generations increased. Nothing out of the ordinary. Why do some of the phenotpes in the simulations not increase while others do? What makes them different?

Differences

This section focuses on the difference between the newt and the snake phenotype. I predict that the phenotype of the species remain close to each other. There is a chance that one species will “escape” and that the difference would be large. I expect (from our calculations) that the begining of the simulation the phenotypes of newts and snakes will be close (in all of them, so a stubby boxplot). As the generations increase I think that boxplot will grow, maybe due to coevolution maybe do to other factors?

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 row(s) containing missing values (geom_path).

The boxplots seem to remain in the center for all of the generations. There some points outside of the boxplots that R considers outliers (mostly they are from a particular simulation(s)). It seems like the phenotypes are closely matching with each other which is a good sign that they are coevolving (hopefully)?

The last step in this rmarkdown is to check out the other paramiters and note if something seems out of the ordinary. I will split it by where the phenotype is supposed to start.

Under 5

For the less than 5 Pairs:

Phenotype and SD Comparisons in Newts and Snakes

Varation in the Phenotype of Newts and Snakes

Differences U5

Under 2

For the less than 2 Pairs:

Phenotype and SD Comparisons in Newts and Snakes

Varation in the Phenotype of Newts and Snakes

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Differences U2

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
## Warning: Removed 22 row(s) containing missing values (geom_path).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

## Warning: Removed 22 row(s) containing missing values (geom_path).

Will try to write more later 08/13/2021